{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c0eb0459a950ce9c",
   "metadata": {},
   "source": [
    "### 1. 数据可视化的重要性\n",
    "\n",
    "#### 1.1 什么是数据可视化？\n",
    "\n",
    "数据可视化是将数据以图形或图表的方式展示，以使人类大脑更容易理解数据。通过将复杂的数据信息转换为直观的图像，数据可视化能够揭示隐藏在数据中的趋势、模式和异常。\n",
    "\n",
    "例如：\n",
    "\n",
    "- 对于销售数据，可以通过折线图观察每月的销售变化趋势。\n",
    "- 对于人口数据，可以通过饼图显示各年龄段所占的比例。\n",
    "\n",
    "#### 1.2 数据可视化在数据分析中的作用\n",
    "\n",
    "- **快速理解数据**：假设有一个包含数千行的销售数据的表格，通过绘制折线图可以一目了然地看到不同月份的销售趋势，而不需要逐行查看表格。\n",
    "- **发现趋势和异常**：例如，使用柱状图对不同产品的销售额进行比较，容易发现哪个产品的表现较好或者是否有异常高的销售波动。\n",
    "- **支持决策制定**：例如，通过散点图可以观察广告支出和销售额之间的关系，从而判断是否应该增加广告预算。\n",
    "- **简化数据展示**：对外展示时，直观的图表更容易被接受和理解，从而有效传达数据分析的结果。\n",
    "\n",
    "#### 1.3 使用 Pandas 进行数据可视化的优势\n",
    "\n",
    "Pandas 具备的数据操作功能与绘图功能的结合，使得它成为初学者进行数据可视化的绝佳工具。下面，我们通过几个具体的代码示例来展示如何使用 Pandas 进行简单的数据可视化。\n",
    "\n",
    "##### 示例代码 1：销售数据的折线图\n",
    "\n",
    "假设我们有一组每月的销售数据，我们想要展示销售额的变化趋势。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6cfa773878505716",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-02T02:59:09.966511Z",
     "start_time": "2024-12-02T02:59:08.691678Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 创建一个包含每月销售数据的 DataFrame\n",
    "data = {\n",
    "    'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'],\n",
    "    'Sales': [1500, 1800, 2500, 2300, 3000, 3500, 4000, 3700, 3800, 4200, 4500, 5000]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 绘制折线图来展示销售额的变化趋势\n",
    "df.plot(x='Month', y='Sales', kind='line', marker='o', linestyle='-', color='b')\n",
    "\n",
    "# 添加图表标题和坐标轴标签\n",
    "plt.title('Monthly Sales Trend')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Sales Amount')\n",
    "plt.grid(True)\n",
    "\n",
    "# 显示图表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c09b8018addd9ca4",
   "metadata": {},
   "source": [
    "**说明：**\n",
    "\n",
    "- 使用 `df.plot()` 方法快速绘制折线图。\n",
    "- `marker='o'` 表示在每个数据点上添加圆点，使得趋势更加明显。\n",
    "- `plt.title()` 和 `plt.xlabel()/plt.ylabel()` 用于设置标题和坐标轴标签，以提高图表的可读性。\n",
    "\n",
    "##### 示例代码 2：产品销售的柱状图\n",
    "\n",
    "假设我们有多个产品的销售数据，想要展示它们之间的销售对比情况。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eda1e854-cf8d-4803-b8bc-14dc826cbf12",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建一个包含不同产品销售数据的 DataFrame\n",
    "data = {\n",
    "    'Product': ['A', 'B', 'C', 'D', 'E'],\n",
    "    'Sales': [3500, 4700, 2900, 5200, 4100]\n",
    "}\n",
    "df_products = pd.DataFrame(data)\n",
    "\n",
    "# 绘制柱状图来对比不同产品的销售情况\n",
    "df_products.plot(x='Product', y='Sales', kind='bar', color='orange', legend=False)\n",
    "\n",
    "# 添加图表标题和坐标轴标签\n",
    "plt.title('Product Sales Comparison')\n",
    "plt.xlabel('Product')\n",
    "plt.ylabel('Sales Amount')\n",
    "\n",
    "# 显示图表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "def6dfb6-03db-44a2-b569-a6e2f6e1f375",
   "metadata": {},
   "source": [
    "**说明：**\n",
    "\n",
    "- `kind='bar'` 表示绘制柱状图。\n",
    "- `legend=False` 表示不显示图例，因为只有一个变量需要展示。\n",
    "\n",
    "##### 示例代码 3：广告支出和销售额的散点图\n",
    "\n",
    "假设我们想要探索广告支出和销售额之间的关系，可以使用散点图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b355a15f-9f3c-40d6-9fe2-b92e7a867709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建一个包含广告支出和销售额数据的 DataFrame\n",
    "data = {\n",
    "    'Advertising Spend': [1000, 1500, 1800, 2000, 2200, 2500, 3000, 3200],\n",
    "    'Sales': [3000, 4000, 4500, 5000, 5200, 6000, 7000, 7500]\n",
    "}\n",
    "df_ads = pd.DataFrame(data)\n",
    "\n",
    "# 绘制散点图来观察广告支出和销售额之间的关系\n",
    "df_ads.plot(x='Advertising Spend', y='Sales', kind='scatter', color='green')\n",
    "\n",
    "# 添加图表标题和坐标轴标签\n",
    "plt.title('Relationship between Advertising Spend and Sales')\n",
    "plt.xlabel('Advertising Spend')\n",
    "plt.ylabel('Sales')\n",
    "\n",
    "# 显示图表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "445c9e06-d554-47aa-a096-4063117141e6",
   "metadata": {},
   "source": [
    "**说明：**\n",
    "\n",
    "- `kind='scatter'` 用于绘制散点图，适合用于观察两个变量之间的关系。\n",
    "- 通过图表可以清晰看到广告支出与销售额之间是否存在线性关系。\n",
    "\n",
    "- 数据可视化能够将原始数据变得更加易于理解。\n",
    "- **Pandas 提供了便捷的 `.plot()` 方法，可以轻松地生成各种类型的图表。**\n",
    "- 通过 Pandas，我们能够快速生成直观的图表，同时能够利用 Matplotlib 进行进一步的美化和自定义。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae5380ef-a335-444c-a155-1a254d41bb6d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
